Data-driven distributed control: Virtual reference feedback tuning in dynamic networks

TL;DR

This paper presents a data-driven approach to synthesize distributed controllers in dynamic networks by leveraging virtual experiments and network identification to optimize model-reference control.

Contribution

It introduces an explicit ideal distributed controller and a novel data-driven synthesis method based on virtual experiments and prediction-error identification.

Findings

The method achieves the same global optimum as the model-reference criterion.

Application to a nine-subsystem network demonstrates effectiveness.

Controller interconnection structure impacts closed-loop performance.

Abstract

In this paper, the problem of synthesizing a distributed controller from data is considered, with the objective to optimize a model-reference control criterion. We establish an explicit ideal distributed controller that solves the model-reference control problem for a structured reference model. On the basis of input-output data collected from the interconnected system, a virtual experiment setup is constructed which leads to a network identification problem. We formulate a prediction-error identification criterion that has the same global optimum as the model-reference criterion, when the controller class contains the ideal distributed controller. The developed distributed controller synthesis method is illustrated on an academic example network of nine subsystems and the influence of the controller interconnection structure on the achieved closed-loop performance is analyzed.